huggingface / huggingface/diffusers

[New feature] A Noise Injection Method for Flux

Aperta
#10,071 10 commenti 1 reazione 0 assegnatari Vedi su GitHub
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Lingua principale
Python
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Descrizione

### New Feature for FluxPipeline
Paper: [Link](https://openreview.net/pdf?id=KzokzKV4JK)
Code : [Link](https://anonymous.4open.science/r/SSNI-F746/README.md)
workflow : [Link](https://www.dropbox.com/scl/fi/hhitjx6lqpqpv8xjx9ikq/Flux-Noise-Injection.json?rlkey=45xnu45j1i5owiwhc7z1hppn1&e=1&dl=0)
This paper introduces Sample-specific Score-aware Noise Injection (SSNI) to improve diffusion-based purification (DBP) methods. Unlike existing approaches that use a fixed noise level (t*) for all samples, SSNI adapts t* based on how noisy or clean each sample is. Using a pre-trained score network, SSNI estimates a sample's deviation from the clean data and adjusts the noise level accordingly.

This have stunning Results with Flux

![spider_with_noise_injection](https://github.com/user-attachments/assets/e87d3da6-6ff5-46c1-b0ff-20c00ad4e541)
![spider_sh](https://github.com/user-attachments/assets/e0f7a469-ae99-4095-9c9d-8cc88ed4691d)

@sayakpaul @yiyixuxu

Guida per i contributori

Apri la guida per i contributori

Direzione di ricerca

Start with FluxPipeline and read the linked paper, reference implementation, and workflow to understand the proposed SSNI method. Done would be an integrated noise-injection feature for Flux with validation against the behavior and results described in the issue.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
python, pytorch
Ambito
machine-learning
Tipo di issue
Funzionalità
Difficoltà
5/5
Tempo stimato
Più di una settimana
Stato di attività
Ferma
Chiarezza
Da chiarire
Idoneità per principianti
25/100

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